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Publication
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| Title of Article |
Single Image Haze Removal Using Deep Cellular Automata Learning |
| Date of Acceptance |
20 May 2020 |
| Journal |
| Title of Journal |
IEEE Access |
| Standard |
ISI |
| Institute of Journal |
https://ieeexplore.ieee.org |
| ISBN/ISSN |
2169-3536 |
| Volume |
2020 |
| Issue |
8 |
| Month |
June |
| Year of Publication |
2023 |
| Page |
103181-103199 |
| Abstract |
Deep learning is one of the most popular approaches to machine learning, which has been
widely used for classi cation. In this paper, we propose a novel learning method based on a combination of
an idea of the deep learning approach and the cellular automata model, called DeepCA for single image haze
removal. DeepCA's learning is divided into two main parts. The rst part is a cellular automata-based deep
feature extraction: multi-layer cellular automata with the rules are used to extract the data feature matrices of
the image, in which the matrices can be divided into several layers. Then, the score matrices were generated
as the model in which was trained by the cellular automata rules. The second part is a decision stage: we used
the score matrices to the mapping between the proper data. For demonstration, we take the single image haze
removal task as an example to con rm the capability of the proposed method. In this regard, the dichromatic
model is chosen as the major model to remove the haze of the image. The multi-layer cellular automata
with the rules work as a mechanical extractor of the light source feature of the hazy image. The decision
stage of DeepCA performs as the recognizer for properly predicting the global light source for dehazing.
This aims to improve the light source and the transmission map that they are important compositions for
haze-free image restoration. For performance evaluation, we perform quantitative and qualitative measures.
For the qualitative performance of the haze removal, DeepCA did not even cause the halo artifact effect
that occurred in other haze removal algorithms. The empirical results in quantitative measures show that
DeepCA improved intensity, color saturation quality, and halo artifact when compared with the state-of-theart
methods. |
| Keyword |
Cellular automata, deep learning, single image, dehazing. |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
true |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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